Summary:
re-apply changes reverted in:
https://github.com/pytorch/pytorch/pull/22412
Also change log_softmax to take positional arguments. Long-term we do want the kwarg-only interface, but seems to currently be incompatible with jit serialization.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22456
Differential Revision: D16097159
Pulled By: nairbv
fbshipit-source-id: 8cb73e9ca18fc66b35b873cf4a574b167a578b3d
Summary:
This change is backwards incompatible in *C++ only* on mean(), sum(), and prod() interfaces that accepted either of:
```
Tensor sum(IntArrayRef dim, bool keepdim=false) const;
Tensor sum(IntArrayRef dim, ScalarType dtype) const;
```
but now to specify both the dim and dtype will require the keepdim parameter:
```
Tensor sum(IntArrayRef dim, bool keepdim=false, c10::optional<ScalarType> dtype=c10::nullopt) const;
```
[xla ci]
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21088
Reviewed By: ailzhang
Differential Revision: D15944971
Pulled By: nairbv
fbshipit-source-id: 53473c370813d9470b190aa82764d0aea767ed74
Summary:
This refactors pybind_utils so we can have all our type-inferring stuff in
1 place (e.g. for #21379)
There is some follow up work to make the error messages better, but I think that's fine to save for another PR.
](https://our.intern.facebook.com/intern/diff/15727002/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21550
Pulled By: driazati
Differential Revision: D15727002
fbshipit-source-id: a6974f2e1e5879f0503a18efc138da31cda7afa2
Summary:
Resolves https://github.com/pytorch/lockdown/issues/18
This implements NamedTuple by taking advantage of the existing `names` field in `TupleType`.
TODO: This currently doesn't retain the NamedTuple-ness through serialization. Discussed with suo offline, we can probably make a way to define an anonymous NamedTuple in script (e.g. `NamedTuple('Foo', [('a', int), ('b', float), ('c', List[float])])` and serialize that
TODO: implement support for calling the constructor with kwargs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21428
Differential Revision: D15741564
Pulled By: jamesr66a
fbshipit-source-id: c077cbcea1880675ca6deb340a9ec78f824a136c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21177
- Integrate c10::ListPtr into IValue and the c10 dispatcher.
- Streamline conversion to/from IValue. Before, we had IValue::to<> and kernel_functor.h had its own ivalue_to_arg_type and return_type_to_ivalue. They are now unified. Also, this means that nested types like Dicts of Lists of Optional of Dict of ... do work as expected now
Differential Revision: D15476433
fbshipit-source-id: bde9df80df20091aa8e6ae17ba7e90abd149b954
Summary:
#19975 was separated by 2 PRs.
This one:
Introduce MemoryFormat argument to the `x.is_contiguous(memory_format=torch.channels_last)` and to the `y = x.contiguous(memory_format=torch.channels_last)` functions.
At this moment both functions just operate with strides and doesn't store any tensor state.
(Original RFC #19092)
-----
Expands functionality of two tensor functions `.is_contiguous` and `.contiguous` (both python and c++ api).
Note: We had several complaints about `.to(memory_format)` function, and decided not to support it.
1. `.contiguous` now support optional keyword-only argument - `memory_format`, which can be either `torch.contiguous_format` or `torch.channels_last`.
- Using `torch.contiguous_format` will preserve existing `.contiguous()` behavior.
- Calling `x.contiguous(memory_format=torch.channels_last)` returns new tensor which maintain same semantical layout (NCHW), but have different memory allocation pattern.
`x.contiguous(memory_format=torch.channels_last)` expects input tensor to be 3d, 4d or 5d; and fails otherwise.
2. `.is_contiguous` now support optional keyword-only argument - `memory_format`, which can be either `torch.contiguous_format` or `torch.channels_last`.
- `x.is_contiguous(memory_format=torch.contiguous_format)` preserves same functionality as `x.is_contiguous()` and remains unchanged.
- `x.is_contiguous(memory_format=torch.channels_last)` returns true if A) input tensor is contiguous in memory AND B) allocated in the memory in NWHC (or similar for 3d,5d) format.
Note: By the end of the phase one `x.is_contiguous(memory_format=torch.channels_last)` will calculate state of the Tensor on every call. This functionality going to be updated later.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20455
Differential Revision: D15341577
Pulled By: VitalyFedyunin
fbshipit-source-id: bbb6b4159a8a49149110ad321109a3742383185d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20372
Implement a Dict type that allows us to abstract away from the concrete implementation used.
The API is similar to std::unordered_map, but behind the scenes we can switch to any map implementation we like. ska::flat_hash_map, google dense map, or any future map implementation with better performance.
Switching such an implementation choice does not have to break backwards compatibility of kernel code using the Dict type.
Reviewed By: zdevito
Differential Revision: D15298234
fbshipit-source-id: b5ad368a9e9516030805cd8f5f1b02e3986933c0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19976
Implement a Dict type that allows us to abstract away from the concrete implementation used.
The API is similar to std::unordered_map, but behind the scenes we can switch to any map implementation we like. ska::flat_hash_map, google dense map, or any future map implementation with better performance.
Switching such an implementation choice does not have to break backwards compatibility of kernel code using the Dict type.
Reviewed By: li-roy
Differential Revision: D15156384
fbshipit-source-id: b9313ec4dd9acb3b6a0035345b6ba4f2a437d1e5
Summary:
* adds TORCH_API and AT_CUDA_API in places
* refactor code generation Python logic to separate
caffe2/torch outputs
* fix hip and asan
* remove profiler_cuda from hip
* fix gcc warnings for enums
* Fix PythonOp::Kind
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19554
Differential Revision: D15082727
Pulled By: kostmo
fbshipit-source-id: 83a8a99717f025ab44b29608848928d76b3147a4
Summary:
Print out the tensor value when throwing the cannot insert tensor with grad error
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19645
Differential Revision: D15057809
Pulled By: eellison
fbshipit-source-id: 3f622ef1322a75c965e780275f1fb447e9acf38d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18092
Previously, tracing required all inputs to be either tensors,
or tuples of tensor. Now, we allow users to pass dicts as well.
Differential Revision: D14491795
fbshipit-source-id: 7a2df218e5d00f898d01fa5b9669f9d674280be3
Summary:
Make it possible to construct a pinned memory tensor without creating a storage first and without calling pin_memory() function. It is also faster, as copy operation is unnecessary.
Supported functions:
```python
torch.rand_like(t, pin_memory=True)
torch.randn_like(t, pin_memory=True)
torch.empty_like(t, pin_memory=True)
torch.full_like(t, 4, pin_memory=True)
torch.zeros_like(t, pin_memory=True)
torch.ones_like(t, pin_memory=True)
torch.tensor([10,11], pin_memory=True)
torch.randn(3, 5, pin_memory=True)
torch.rand(3, pin_memory=True)
torch.zeros(3, pin_memory=True)
torch.randperm(3, pin_memory=True)
torch.empty(6, pin_memory=True)
torch.ones(6, pin_memory=True)
torch.eye(6, pin_memory=True)
torch.arange(3, 5, pin_memory=True)
```
Part of the bigger: `Remove Storage` plan.
Now compatible with both torch scripts:
` _1 = torch.zeros([10], dtype=6, layout=0, device=torch.device("cpu"), pin_memory=False)`
and
` _1 = torch.zeros([10], dtype=6, layout=0, device=torch.device("cpu"))`
Same checked for all similar functions `rand_like`, `empty_like` and others
It is fixed version of #18455
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18952
Differential Revision: D14801792
Pulled By: VitalyFedyunin
fbshipit-source-id: 8dbc61078ff7a637d0ecdb95d4e98f704d5450ba
Summary:
Almost there, feel free to review.
these c10 operators are exported to _caffe2 domain.
TODO:
- [x] let the onnx checker pass
- [x] test tensor list as argument
- [x] test caffe2 backend and converter
- [x] check the c10 schema can be exported to onnx
- [x] refactor the test case to share some code
- [x] fix the problem in ONNX_ATEN_FALLBACK
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18210
Reviewed By: zrphercule
Differential Revision: D14600916
Pulled By: houseroad
fbshipit-source-id: 2592a75f21098fb6ceb38c5d00ee40e9e01cd144
Summary:
Make it possible to construct a pinned memory tensor without creating a storage first and without calling pin_memory() function. It is also faster, as copy operation is unnecessary.
Supported functions:
```python
torch.rand_like(t, pin_memory=True)
torch.randn_like(t, pin_memory=True)
torch.empty_like(t, pin_memory=True)
torch.full_like(t, 4, pin_memory=True)
torch.zeros_like(t, pin_memory=True)
torch.ones_like(t, pin_memory=True)
torch.tensor([10,11], pin_memory=True)
torch.randn(3, 5, pin_memory=True)
torch.rand(3, pin_memory=True)
torch.zeros(3, pin_memory=True)
torch.randperm(3, pin_memory=True)
torch.empty(6, pin_memory=True)
torch.ones(6, pin_memory=True)
torch.eye(6, pin_memory=True)
torch.arange(3, 5, pin_memory=True)
```
Part of the bigger: `Remove Storage` plan.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18455
Reviewed By: ezyang
Differential Revision: D14672084
Pulled By: VitalyFedyunin
fbshipit-source-id: 9d0997ec00f59500ee018f8b851934d334012124
